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Machine Learning in Java
Astheamountofdataintheworldcontinuestogrowatanalmostincomprehensiblerate,beingabletounderstandandprocessdataisbecomingakeydifferentiatorforcompetitiveorganizations.Machinelearningapplicationsareeverywhere,fromself-drivingcars,spamdetection,documentsearch,andtradingstrategies,tospeechrecognition.Thismakesmachinelearningwell-suitedtothepresent-dayeraofbigdataandDataScience.Themainchallengeishowtotransformdataintoactionableknowledge.MachineLearninginJavawillprovideyouwiththetechniquesandtoolsyouneed.Youwillstartbylearninghowtoapplymachinelearningmethodstoavarietyofcommontasksincludingclassification,prediction,forecasting,marketbasketanalysis,andclustering.ThecodeinthisbookworksforJDK8andabove,thecodeistestedonJDK11.Movingon,youwilldiscoverhowtodetectanomaliesandfraud,andwaystoperformactivityrecognition,imagerecognition,andtextanalysis.Bytheendofthebook,youwillhaveexploredrelatedwebresourcesandtechnologiesthatwillhelpyoutakeyourlearningtothenextlevel.Byapplyingthemosteffectivemachinelearningmethodstoreal-worldproblems,youwillgainhands-onexperiencethatwilltransformthewayyouthinkaboutdata.
目錄(286章)
倒序
- coverpage
- Title Page
- Contributors
- About the authors
- About the reviewer
- Packt is searching for authors like you
- About Packt
- Why subscribe?
- Packt.com
- Preface
- Who this book is for
- What this book covers
- To get the most out of this book
- Download the example code files
- Download the color images
- Conventions used
- Get in touch
- Reviews
- Applied Machine Learning Quick Start
- Machine learning and data science
- Solving problems with machine learning
- Applied machine learning workflow
- Data and problem definition
- Measurement scales
- Data collection
- Finding or observing data
- Generating data
- Sampling traps
- Data preprocessing
- Data cleaning
- Filling missing values
- Remove outliers
- Data transformation
- Data reduction
- Unsupervised learning
- Finding similar items
- Euclidean distances
- Non-Euclidean distances
- The curse of dimensionality
- Clustering
- Supervised learning
- Classification
- Decision tree learning
- Probabilistic classifiers
- Kernel methods
- Artificial neural networks
- Ensemble learning
- Evaluating classification
- Precision and recall
- Roc curves
- Regression
- Linear regression
- Logistic regression
- Evaluating regression
- Mean squared error
- Mean absolute error
- Correlation coefficient
- Generalization and evaluation
- Underfitting and overfitting
- Train and test sets
- Cross-validation
- Leave-one-out validation
- Stratification
- Summary
- Java Libraries and Platforms for Machine Learning
- The need for Java
- Machine learning libraries
- Weka
- Java machine learning
- Apache Mahout
- Apache Spark
- Deeplearning4j
- MALLET
- The Encog Machine Learning Framework
- ELKI
- MOA
- Comparing libraries
- Building a machine learning application
- Traditional machine learning architecture
- Dealing with big data
- Big data application architecture
- Summary
- Basic Algorithms - Classification Regression and Clustering
- Before you start
- Classification
- Data
- Loading data
- Feature selection
- Learning algorithms
- Classifying new data
- Evaluation and prediction error metrics
- The confusion matrix
- Choosing a classification algorithm
- Classification using Encog
- Classification using massive online analysis
- Evaluation
- Baseline classifiers
- Decision tree
- Lazy learning
- Active learning
- Regression
- Loading the data
- Analyzing attributes
- Building and evaluating the regression model
- Linear regression
- Linear regression using Encog
- Regression using MOA
- Regression trees
- Tips to avoid common regression problems
- Clustering
- Clustering algorithms
- Evaluation
- Clustering using Encog
- Clustering using ELKI
- Summary
- Customer Relationship Prediction with Ensembles
- The customer relationship database
- Challenge
- Dataset
- Evaluation
- Basic Naive Bayes classifier baseline
- Getting the data
- Loading the data
- Basic modeling
- Evaluating models
- Implementing the Naive Bayes baseline
- Advanced modeling with ensembles
- Before we start
- Data preprocessing
- Attribute selection
- Model selection
- Performance evaluation
- Ensemble methods – MOA
- Summary
- Affinity Analysis
- Market basket analysis
- Affinity analysis
- Association rule learning
- Basic concepts
- Database of transactions
- Itemset and rule
- Support
- Lift
- Confidence
- Apriori algorithm
- FP-Growth algorithm
- The supermarket dataset
- Discover patterns
- Apriori
- FP-Growth
- Other applications in various areas
- Medical diagnosis
- Protein sequences
- Census data
- Customer relationship management
- IT operations analytics
- Summary
- Recommendation Engines with Apache Mahout
- Basic concepts
- Key concepts
- User-based and item-based analysis
- Calculating similarity
- Collaborative filtering
- Content-based filtering
- Hybrid approach
- Exploitation versus exploration
- Getting Apache Mahout
- Configuring Mahout in Eclipse with the Maven plugin
- Building a recommendation engine
- Book ratings dataset
- Loading the data
- Loading data from a file
- Loading data from a database
- In-memory databases
- Collaborative filtering
- User-based filtering
- Item-based filtering
- Adding custom rules to recommendations
- Evaluation
- Online learning engine
- Content-based filtering
- Summary
- Fraud and Anomaly Detection
- Suspicious and anomalous behavior detection
- Unknown unknowns
- Suspicious pattern detection
- Anomalous pattern detection
- Analysis types
- Pattern analysis
- Transaction analysis
- Plan recognition
- Outlier detection using ELKI
- An example using ELKI
- Fraud detection in insurance claims
- Dataset
- Modeling suspicious patterns
- The vanilla approach
- Dataset rebalancing
- Anomaly detection in website traffic
- Dataset
- Anomaly detection in time series data
- Using Encog for time series
- Histogram-based anomaly detection
- Loading the data
- Creating histograms
- Density-based k-nearest neighbors
- Summary
- Image Recognition with Deeplearning4j
- Introducing image recognition
- Neural networks
- Perceptron
- Feedforward neural networks
- Autoencoder
- Restricted Boltzmann machine
- Deep convolutional networks
- Image classification
- Deeplearning4j
- Getting DL4J
- MNIST dataset
- Loading the data
- Building models
- Building a single-layer regression model
- Building a deep belief network
- Building a multilayer convolutional network
- Summary
- Activity Recognition with Mobile Phone Sensors
- Introducing activity recognition
- Mobile phone sensors
- Activity recognition pipeline
- The plan
- Collecting data from a mobile phone
- Installing Android Studio
- Loading the data collector
- Feature extraction
- Collecting training data
- Building a classifier
- Reducing spurious transitions
- Plugging the classifier into a mobile app
- Summary
- Text Mining with Mallet - Topic Modeling and Spam Detection
- Introducing text mining
- Topic modeling
- Text classification
- Installing Mallet
- Working with text data
- Importing data
- Importing from directory
- Importing from file
- Pre-processing text data
- Topic modeling for BBC News
- BBC dataset
- Modeling
- Evaluating a model
- Reusing a model
- Saving a model
- Restoring a model
- Detecting email spam
- Email spam dataset
- Feature generation
- Training and testing
- Model performance
- Summary
- What Is Next?
- Machine learning in real life
- Noisy data
- Class unbalance
- Feature selection
- Model chaining
- The importance of evaluation
- Getting models into production
- Model maintenance
- Standards and markup languages
- CRISP-DM
- SEMMA methodology
- Predictive model markup language
- Machine learning in the cloud
- Machine learning as a service
- Web resources and competitions
- Datasets
- Online courses
- Competitions
- Websites and blogs
- Venues and conferences
- Summary
- Other Books You May Enjoy
- Leave a review - let other readers know what you think 更新時間：2021-06-10 19:30:50
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